The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Critical assessment of automated flow cytometry data analysis techniques
Open accessAbstract
In this analysis, the authors directly compared the performance of flow cytometry data processing algorithms to manual gating approaches. The results offer information of practical utility about the performance of the algorithms as applied to different data sets and challenges. Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks: (i) mammalian cell population identification, to determine whether automated algorithms can reproduce expert manual gating and (ii) sample classification, to determine whether analysis pipelines can identify characteristics that correlate with external variables (such as clinical outcome). This analysis presents the results of the first FlowCAP challenges. Several methods performed well as compared to manual gating or external variables using statistical performance measures, which suggests that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis.
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The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954
(2007) · doi:10.5555/1283383.1283494 · Cited by 7,364
An efficient k-means clustering algorithm: analysis and implementation
Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017616 · Cited by 5,660
Bendall, S. C., Simonds, E. F., Qiu, P., Amir, E.-A. D., Krutzik, P. O., Finck, R., Bruggner, R. V., Melamed, R., Trejo, A., Ornatsky, O. I., et al. (2011) · Science · doi:10.1126/science.1198704 · Cited by 2,279
Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE
Qiu, P., Simonds, E. F., Bendall, S. C., Gibbs, K. D., Bruggner, R. V., Linderman, M. D., Sachs, K., Nolan, G. P., & Plevritis, S. K. (2011) · Nature Biotechnology · doi:10.1038/nbt.1991 · Cited by 956
MIFlowCyt: The minimum information about a flow cytometry experiment
Lee, J. A., Spidlen, J., Boyce, K., Cai, J., Crosbie, N., Dalphin, M., Furlong, J., Gasparetto, M., Goldberg, M., Goralczyk, E. M., et al. (2008) · Cytometry Part A · doi:10.1002/cyto.a.20623 · Cited by 533
Self-citationTowards a rigorous assessment of systems biology models: the DREAM3 challenges
Prill, R. J., Marbach, D., Saez-Rodriguez, J., Sorger, P. K., Alexopoulos, L. G., Xue, X., Clarke, N. D., Altan-Bonnet, G., & Stolovitzky, G. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009202 · Cited by 439
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Levine, J. H., Simonds, E. F., Bendall, S. C., Davis, K. L., Amir, E.-A. D., Tadmor, M. D., Litvin, O., Fienberg, H. G., Jager, A., Zunder, E. R., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.047 · Cited by 2,303
FlowSOM: Using self‐organizing maps for visualization and interpretation of cytometry data
Van Gassen, S., Callebaut, B., Van Helden, M. J., Lambrecht, B. N., Demeester, P., Dhaene, T., & Saeys, Y. (2015) · Cytometry Part A · doi:10.1002/cyto.a.22625 · Cited by 1,919
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Spitzer, M. H., & Nolan, G. P. (2016) · Cell · doi:10.1016/j.cell.2016.04.019 · Cited by 1,199
Orchestrating single-cell analysis with Bioconductor
Amezquita, R. A., Lun, A. T. L., Becht, E., Carey, V. J., Carpp, L. N., Geistlinger, L., Marini, F., Rue-Albrecht, K., Risso, D., Soneson, C., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0654-x · Cited by 945
Self-citationGuidelines for the use of flow cytometry and cell sorting in immunological studies (third edition)
Cossarizza, A., Chang, H., Radbruch, A., Abrignani, S., Addo, R., Akdis, M., Andrä, I., Andreata, F., Annunziato, F., Arranz, E., et al. (2021) · European Journal of Immunology · doi:10.1002/eji.202170126 · Cited by 531
Metadata
- Authors
- The FlowCAP Consortium, Aghaeepour, N., The DREAM Consortium, Finak, G., Hoos, H., Mosmann, T. R., Brinkman, R., Gottardo, R., & Scheuermann, R. H.
- Venue
- Nature Methods
- Published
- 2013-02-10
- Volume
- 10
- Issue
- 3
- Pages
- 228-238
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2112493011
- Cited by
- 598
- References
- 8
- FWCI
- 38.15
- Type
- journal article
- Language
- en
- Open access
- hybrid
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